AI Detection for Damaged Solar Panels (EPRI)
Adam Hubbard, Julian Shelton, Thomas Clemmons
University of Tennessee in Chattanooga
Artificial Intelligence
Supervised learning is a type of machine learning in which an algorithm learns from labeled data. In this process, the algorithm is presented with inputs and corresponding outputs and is trained to map inputs to outputs. The goal of supervised learning is to build a model that can accurately predict the output for new inputs.
Convolutional Neural Networks, or CNNs, are a type of neural network commonly used in image recognition and classification tasks. CNNs are particularly well-suited for this type of problem because they are able to automatically learn features from images, rather than relying on manual feature engineering. This is accomplished through the use of convolutional layers, which apply a series of filters to the input image to extract relevant features.
In the context of solar panel classification, supervised learning and CNNs can be used to accurately classify images of solar panels based on their condition. This is important because identifying damaged or dirty solar panels can help to prevent energy loss and improve overall system efficiency.
To use supervised learning and CNNs for solar panel classification, a dataset of labeled images is required. This dataset includes images of solar panels in various conditions, in this case broken, dirty, microcracks, Hotspots, and good. The CNN is then trained on this dataset to learn the features that distinguish between these different conditions.
During training, the CNN adjusts the weights of its filters to improve its ability to correctly classify the images in the dataset. This process is repeated until the CNN is able to accurately classify images it has never seen before.
Once the CNN is trained, it can be used to classify new images of solar panels based on their condition. The input image is fed into the Convolutional Neural Network, which applies its filters and outputs a prediction of the panel's condition.
Overall, the use of supervised learning and CNNs for solar panel classification has the potential to greatly improve the efficiency and reliability of solar energy systems. By accurately identifying damaged or dirty panels, system operators can take steps to address these issues and maximize energy output.
